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Transformers Interpret

Transformers Interpret is an open-source Python library for explaining Hugging Face Transformers models. It provides text and computer-vision explainers, with visualizations that can be displayed in notebooks or saved as PNG and HTML files.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Transformers Interpret — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Transformers model is already part of a Python workflow and you want attribution-based views of text or computer-vision predictions.

Who it is for

  • Developers and ML practitioners using Hugging Face Transformers who want to inspect model predictions.
  • Teams that need notebook-based or file-based views of text or computer-vision explanations.

Documented uses

  • Inspect word-level contributions to a text-classification prediction.
  • Examine attributions for a class other than the model’s predicted class.
  • Create notebook visualizations or save explanation views as PNG and HTML files.
  • Add explanation workflows around an existing Transformers model.
Before you choose

Requirements & limits

The public GitHub repository lists an Apache-2.0 license and documents installation with pip.

  • The library is designed specifically for the Hugging Face Transformers package.
  • The documented text workflow requires both a model and its tokenizer.
  • The repository documents notebook display and PNG or HTML visualization outputs.
  • Attribution scores indicate contribution toward or against a class; interpretation remains a separate task.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate and visualize text-classification attributions

Inspect word-level attributions for a Transformers sequence-classification model and save an HTML visualization.

Before you start

  • Python environment with the transformers-interpret package installed.
  • A Hugging Face Transformers sequence-classification model and its tokenizer.

Steps

  1. Initialize the model and tokenizer with Transformers.

  2. Create a SequenceClassificationExplainer using the model and tokenizer.

  3. Pass text to the explainer to obtain word attributions.

  4. Call visualize() with an HTML filepath to save the attribution view.

Expected outcome

Word-level attribution scores and an HTML visualization of the explanation.

Guide limitations

  • The documented example covers text sequence classification; it does not establish guidance for every model architecture or task.
  • Attributions describe contributions to a class and are not, by themselves, proof that a model is correct.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
16 October 2026
Original website title
cdpierse/transformers-interpret · Official homepage title · retained in its source language
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